Nonequilibrium reaction networks (NRNs) underlie most biological functions. Despite their diverse dynamic properties, NRNs share the signature characteristics of persistent probability fluxes and continuous energy dissipation even in the steady state. Dynamics of NRNs can be described at different coarse-grained levels. Our previous work showed that the apparent energy dissipation rate at a coarse-grained level follows an inverse power-law dependence on the scale of coarse-graining. The scaling exponent is determined by the network structure and correlation of stationary probability fluxes. However, it remains unclear whether and how the (renormalized) flux correlation varies with coarse-graining. Following Kadanoff's real space renormalization group (RG) approach for critical phenomena, we address this question by developing a state-space renormalization group theory for NRNs, which leads to an iterative RG equation for the flux correlation function. In square and hypercubic lattices, we solve the RG equation exactly and find two types of fixed point solutions. There is a family of nontrivial fixed points where the correlation exhibits power-law decay, characterized by a power exponent that can take any value within a continuous range. There is also a trivial fixed point where the correlation vanishes beyond the nearest neighbors. The power-law fixed point is stable if and only if the power exponent is less than the lattice dimension n. Consequently, the correlation function converges to the power-law fixed point only when the correlation in the fine-grained network decays slower than r^{-n} and to the trivial fixed point otherwise. If the flux correlation in the fine-grained network contains multiple stable solutions with different exponents, the RG iteration dynamics select the fixed point solution with the smallest exponent. The analytical results are supported by numerical simulations. We also discuss a possible connection between the RG flows of flux correlation with those of the Kosterlitz-Thouless transition.
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http://dx.doi.org/10.1103/PhysRevE.105.044140 | DOI Listing |
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January 2025
HIGN, New York University Rory Meyers College of Nursing, and Division of Geriatric Medicine and Palliative Care, New York University Grossman School of Medicine, New York, NY, USA.
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Clin Rheumatol
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Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1 Shuaifuyuan, Beijing, 100730, China.
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View Article and Find Full Text PDFSci Rep
January 2025
Department of Mathematics, Dayalbagh Educational Institute, Agra, India.
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January 2025
Division of Dentistry, School of Medical Sciences, The University of Manchester, Manchester, M13 9PL, UK.
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View Article and Find Full Text PDFComput Biol Med
January 2025
SCOPIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science, Crta. Valldemossa, Km 7.5, Palma, E-07122, Spain; Health Research Institute of the Balearic Islands (IdISBa), Palma, E-07122, Spain; Laboratory for Artificial Intelligence Applications at UIB (LAIA@UIB), Palma, E-07122, Spain; Artificial Intelligence Research Institute of the Balearic Islands (IAIB), Palma, E-07122, Spain. Electronic address:
Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a high global prevalence and places a significant burden on healthcare systems, especially in resource-limited regions. Automated classification of sickle cells in blood images is crucial, allowing the specialist to reduce the effort required and avoid errors when quantifying the deformed cells and assessing the severity of a crisis.
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